Cultural tourism taxonomies and folksonomies Chris Tweed School of Planning, Architecture and Civil Engineering Queen’s University Belfast

The problem

Aims and objectives To develop a comprehensive map of the elements of cultural heritage that attract different people to towns and cities by: –identifying and categorising a comprehensive range of cultural attractors –identifying interests and motivators for different types of tourists –identifying relations of attraction between attractors and interests To identify governance strategies based on analysis of the relations

Existing classification taxonomies Prentice’s typology—23 main types –natural history attractions, science based attractions, attractions concerned with primary production, craft centres and craft workshops, attractions concerned with manufacturing industry, transport attractions, socio-cultural attractions, attractions associated with historic persons, performing arts attractions, pleasure gardens, theme parks, galleries, festivals and pageants, fieldsports, stately and ancestral homes, religious attractions, military attractions, genocide monuments, towns and townscape, villages and hamlets, countryside and treasured landscapes, seaside resorts and 'seascapes', regions not all cultural, not all urban, though difficult to exclude from “culture”

Existing classification taxonomies PICTURE D7 list—16 main types, with many sub-types –tangible heritage within the surrounding environment/landscapes, cultural clusters, individual monuments, public spaces, heritage attractions, traditional local markets, festivals and special events, traditional crafts, languages (living and used/signs), information systems, industry and commerce, religious sites, iconic buildings, sites associated with historic or legendary events and famous people, sport and leisure activities, traditional food and drinks, modern pop culture offers a list which is more closely related to PICTURE tasks

Classification problems Dewey, 200: Religion 210 Natural theology 220 Bible 230 Christian theology 240 Christian moral & devotional theology 250 Christian orders & local church 260 Christian social theology 270 Christian church history 280 Christian sects & denominations 290 Other religions

Classification problems US Library of Congress D: History (general) DA: Great Britain DB: Austria DC: France DD: Germany DE: Mediterranean DF: Greece DG: Italy DH: Low Countries DJ: Netherlands DK: Former Soviet Union DL: Scandinavia DP: Iberian Peninsula DQ: Switzerland DR: Balkan Peninsula DS: Asia DT: Africa DU: Oceania DX: Gypsies

Existing classification approaches are … designed for specific purposes object-oriented rather than experience-oriented generally too complicated for anyone without specialised training require strict control over the creation of new entities and branches become fixed and inflexible

A design analogy

From taxonomy to folksonomy

Folksonomy folksonomy = folk + taxonomy Also known as ethnoclassification Main feature is user-generated metadata in the form of tags or keywords Tags exist in a flat namespace with no hierarchy It is a bottom-up as opposed to a top-down approach A folksonomy is more about categorisation than classification

Traditional hierarchy

Hierarchy with links

Just links (no hierarchy)

Limitations of folksonomies Ambiguity The same tag may be used in different ways Different tags may be used for the same concept Acronyms not differentiated from actual words, e.g. ANT Imprecision Lack of controlled vocabularies allows great inaccuracies Chaos Lack of structure creates possibility of chaos

Strengths of folksonomies Accessibility Low barriers to entry for general public Low cognitive costs Folksonomies reflect response of general public Generate socially shared data Dynamic Feedback on tagging is immediate Tags evolve to reflect new concerns

Developing a folksonomy of cultural tourism attractions Develop a Web-based questionnaire providing opportunities for visitors to record their: descriptions feelings evaluations and relevant personal details for selected sites (represented as Web pages) Analyse data for emergent relations across attractions and visitors Provide tools for browsing explicit and implicit relations

Potential added value

Work to be done Finalise Web questionnaire and database Identify selected sites Collect data from tourists Analyse data